MY201868A - Neural network processor using compression and decompression of activation data to reduce memory bandwidth utilization - Google Patents

Neural network processor using compression and decompression of activation data to reduce memory bandwidth utilization

Info

Publication number
MY201868A
MY201868A MYPI2019006051A MYPI2019006051A MY201868A MY 201868 A MY201868 A MY 201868A MY PI2019006051 A MYPI2019006051 A MY PI2019006051A MY PI2019006051 A MYPI2019006051 A MY PI2019006051A MY 201868 A MY201868 A MY 201868A
Authority
MY
Malaysia
Prior art keywords
data
chunk
dnn
neural network
decompression
Prior art date
Application number
MYPI2019006051A
Inventor
Leon Corkery Joseph
Eliot Lundell Benjamin
Marvin Wall Larry
Balling Mcbride Chad
Ashok Ambardekar Amol
Petre George
D Cedola Kent
Bobrov Boris
Original Assignee
Microsoft Technology Licensing Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Microsoft Technology Licensing Llc filed Critical Microsoft Technology Licensing Llc
Publication of MY201868A publication Critical patent/MY201868A/en

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Abstract

A deep neural network ("DNN") module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit (200) can receive an uncompressed chunk of data (202) generated by a neuron in the DNN module. The compression unit generates a mask portion (208) and a data portion (210) of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit (500) can receive a compressed chunk of data (204) from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion (208) and the data portion (210). This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption. (Figure 4)
MYPI2019006051A 2017-04-17 2018-04-16 Neural network processor using compression and decompression of activation data to reduce memory bandwidth utilization MY201868A (en)

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